 advanced feature detection on a preset collection of images. It integrates the robust computer vision capabilities of OpenCV to apply different feature detection algorithms, providing users with immediate visual feedback on the results of various image analysis techniques. Key functionalities feature detection algorithms. Users can choose from a list of implemented algorithms, Fast, Harris, and Sheet Amazi to analyze the images. Preloaded image selection. A drop-down menu allows users to select from a range of preloaded images available on the server. Preloaded image processing. The server processes images using a selected algorithm and instantly displays the processed output alongside the original image. Efficient processing. Processed images are stored to avoid redundant computations, speeding up the retrieval of previously requested image analysis. Technical components flask back end. The application runs on flask, a lightweight and flexible Python web server framework that facilitates rapid development. OpenCV integration. OpenCV, a comprehensive open source library for computer vision tasks, is used for executing feature detection algorithms efficiently. Ginger 2 templating. Flask rendering engine. Ginger 2. Dynamically generates HTML content based on server-side data, such as the list of images and processing results. JavaScript enhancements. Client-side scripting enhances the user experience by dynamically updating the web interface in response to user actions. User experience. The app is tailored for simplicity and ease of use, catering to educators, students, and hobbyists interested in computer vision. By abstracting away the complexities of the algorithms, it provides an educational tool that visualizes different feature detection methods with just a few clicks. Deployment and usage after setting up the flask server, users can access the app through their web browser. The straightforward interface requires no special instructions, making it straightforward to compare and learn about various feature detection techniques. Future directions. While currently focused on a predetermined set of images, the app can be expanded to allow users to upload their images in the future. External feature detection methods and performance optimizations may also be incorporated to enhance the app's capabilities in user engagement. Yeah, just writing a HTML description for this app, and we will be ready to deploy. Let's get into it, not if we need those separate sections, but okay, check it out. Right, so we have this description. The app is still working. It can actually have a different detector type as default, old value in HTML, have a selected option, select, choose a detector type, yeah, those are coming from the back end. And we do not have a submit button. Yeah, it's more like it, this is actually what we are doing, because all those dropdowns are being populated by the back end. It's not in the HTML, so if you want to change the default value here, you have to do it The white types have this, I have default there, we actually want it to be the sheet Tomasi option as such, and then in return, render template, we have the index HTML, and the detector types, and also it could be a comma that gets still fast, HTML. So this default detector, where was not being used, how to use default detector, still in HTML, select, take the select, name the detector, or this is the same, okay, this one, and just change that code, but placing it as a skill, isn't it, and still not working while, control of five, I would have expected this to be sheet Tomasi, ah, let's see. Something wrong, looks like there's some sort of race condition where it's being selected first, but then it's changing to a different default, which is not cool, and should we change that to, yeah, the JavaScript seem to have nothing to do with it. Choose the detected type, default detector is not working, and make sure there's no selected option in HTML, and the selected sheet, there's some debugging options. Make sure that the flash group is indeed being hit, and that the context is being passed to template, you can add a print statement in the flash group to debug, okay, obviously work okay, nothing to debug there really, yeah, so why, says default detector set to sheet Tomasi, then proceed, proceed using fast detector, why, that's not cool, and that's okay, that's fine, well actually, you can get rid of, it's actually working, okay, just focus on these two sentences. From the logs you posted, it looks like the flash application is working as intended in terms of processing images and serving the processed image files. The log indicates that the default detector has been set to sheet Tomasi, and that an image has been processed using the fast detector after a post request to process underscore image. If the issue is that sheet Tomasi, option is not being shown as the default selected option in the dropdown when the page loads, but the server log shows the default detector set to sheet Tomasi, then the problem might be on the client side. In the HTML template, or in the way the browser renders the dropdown, make sure the logic in your HTML template matches the intended default as set by the server side variable. Your dropdown should be set up something like this, if there's JavaScript that manipulates selections on or after page load, it might be changing the selection away from the default. Disable any such JavaScript to see if the issue persists. If there's JavaScript that manipulates selections on or after page load, it might be changing the selection away from the default. Disable any such JavaScript to see if the issue persists. Yes, the JavaScript function fetch detector types, you posted fetchers detector types from the server and updates the detector select dropdown. The function also sets the default value for the detector select dropdown based on the response. If the default underscore detector is not being set properly on the client side dropdown, it might be due to the following line. This line sets the dropdown's value to the first detector in the list returned from the server of detector select value is full C, which is often the case when the dropdown has just been populated and no selection has been made yet. However, this code doesn't account for setting the default detector based on the server's default underscore detector value. Remove that code. It didn't fix the problem. This line sets the dropdown's value to the first detector in the list returned from the server of detector select value is full C, which is often the case when the dropdown has just been populated and no selection has been made yet. However, this code doesn't account for setting the default detector based on the server's default underscore detector value. To fix this, you would need to modify the function to set the dropdown value to the default underscore detector that the server indicates should be the default. However, from the current code, it looks like default underscore detector isn't being used in the client side logic. You should ensure that default underscore detector is part of the JSON response from the server and then use it as follows. For this to work, your fetch underscore detector's server side route would need to include the default underscore detector in its JSON response. Make sure the server is sending the correct default detector within the response, and then modify the JavaScript to use this value when setting the default selection. This should resolve any issues with setting the default detector in your dropdown menu. You're not ready to do this. It's a bit weird. Okay, now it's working, so setting the default detector twice. Well, that should be okay, as long as it's actually working. Those levels for sheet to must be in red. Image feature detection app overview key functionalities feature detection algorithms. Fast, Harris. Sheet to Massey preloaded image selection via dropdown menu instant image processing and display results. Efficient processing with stored processed images. Technical components flash back in for easy server side management. Open CV integration for feature detection operations. Ginger to templating for dynamic HTML content. JavaScript enhancements for interactive UI updates. User experience. The application simplifies the process of exploring computer vision algorithms offering a user friendly interface suitable for educational purposes and quick demonstrations of feature detection technology. Deployment and usage the app is deployed on a flask server and accessible via web browser ready to demonstrate feature detection with minimal setup. Future directions, future updates may include user image uploads, additional algorithms, and further optimizations to enhance the application's interactivity and educational value. Start deploying in a sec. Color change. And so that description is okay. This is what we currently have in production. So this new tool will go over here. Feature image feature extraction. Yeah, this one here. And we should be ready to deploy. I like how Harris is labeling colors. We have a general description as well. Yeah, this one happened to the next HTML. This bit is important. That's right. We have the description there. We have this thing happening twice. I think one of these bits can go. Yep, that's still working. Okay. The thing is an option to select the region of interest. This was happening live. GPPT just changed its layout right now in default. So they removed all this different option for GPT-4. And the whole thing just has the one option that includes daily browsing, internet browsing, and all analysis. I think what used to be called code interpreter, I think it's the same thing as analysis. And it has an option to upload files, try this. Oops, just happened. Yeah, they changed the layout quite a bit. Did I just lose the chat? Right, GPPT can now browse the web, analyze data, generate images. This could build this on our build into GPT-4. No need to select that available for plus users. Okay, still have the chat from today. Yes, can actually add this. Can just paste. This is the front end suggestions. So it says it's still as slow as before. That's for sure. The front end of your image feature detection application presents a clean and straightforward interface. Here are a few suggestions to enhance usability and aesthetics. Consider adding labels are legend to distinguish between the original image and the processed image. This helps users understand what they're looking at more quickly. I think that's pretty straightforward as it is. Ensure that the layout is responsive so that it adjusts well to different screen sizes, especially for mobile devices. Using CSS frameworks like Bootstrap can simplify this process. Adding a brief instruction section or tooltips can guide new users on how to use the application effectively. Yeah, we have some description and functionalities at the bottom. Use size, color, and layout to create a visual hierarchy that naturally guides the user's eyes through the interface. For example, primary actions or important information should stand out more. That's generic stuff. If the processing takes time, implement a loading indicator or a progress bar to inform users that the process is ongoing. It's pretty quick, so we don't need that. Yeah, there's some future notes as well. If the image has text in it, the detector is focusing on the text, well at least the highest one. Sheetoumasi does both the image features and the text and fast. Well, the fast algorithm never really works very well. That's why I changed the default to be Harris, but this particular image is also not great. Now a future thing, we have these future directions. We want to be able to select a ROI, the region of interest. So this algorithm works better. Selection, future direction. Let's see what the bot thinks, especially the new improved GPT-4 region of interest. That's right. Region of interest, ROI, selection. To provide users with more precise analysis capabilities, we plan to implement a region of interest selection tool within the application. This feature will allow users to specify particular areas of the image for focused feature detection. By simply clicking and dragging over the desired area, users can define the ROI, which can then be used to apply feature detection algorithms exclusively within this region. This is particularly useful in scenarios where users are interested in analyzing specific parts of an image, such as identifying features in a particular object or segmenting a specific structure within a medical image. That's not a bad description of it. Implementing ROI selection can enhance the application's utility significantly, especially for users who deal with complex images where global feature detection might yield too much noise. In medical imaging, for instance, focusing on a tumor or a specific brain region could provide more meaningful insights than processing the entire image. Okay, the description is too lengthy to might simplify the bit. ROI selection could be accompanied by additional tools for adjusting the size and shape of a selected area, as well as the ability to move the ROI around the image for comparative analysis. With this enhanced control, the application could serve an even broader range of use cases and provide more valued researchers, clinicians and hobbyists interested in targeted image analysis. In terms of technical implementation, ROI selection can be achieved using client-side scripting, where mass events are used to draw a rectangle over the image. This rectangle's coordinates can then be sent to the server-side flask application, where openCV can process only the selected image region. Adding this feature would certainly make the application more powerful and versatile for users requiring detailed image analysis capabilities. Sure, over here is just do the job. Yes, so there, that's the general liner. Future updates may include user image uploads, additional algorithms and further optimizations to enhance the application's interactivity and educational value. Yes, so, yeah, we want to allow uploads and have a ROI selection option. What else? So, say this image here, which is an epileptic seizure EEG, short direction processing for different image types. For example, EEG signal is specifically one with seizures or specifically for seizure, spike detection, send messages and up error. It's a bit hard. Yeah, so for these images, you want to be able to select the region of interest. For this type of images, you want to be able to have more optimization features. Optimization for varied image types, recognizing the diverse needs of our user base, we aim to optimize the application for a variety of image types beyond standard photographs. Once such advancement will target the analysis of EEG signal images, focusing on the detection of seizure spikes, the critical aspect of neurological diagnostics. Unlike the language it uses. Seizure spike detection in EEG signals, EEG, electroencephalogram, signals provide a wealth of information about brain activity and are crucial in diagnosing and monitoring neurological disorders like epilepsy. Our plan feature will introduce algorithms specifically tuned to identify seizure-related anomalies in EEG images. By applying specialized filtering and feature detection techniques, we can assist healthcare professionals in rapidly pinpointing areas of interest in EEG data. Yeah, the language, but to be, I'll ask you to rewrite it. Custom algorithm development. This will involve developing or integrating custom algorithms tailored for the unique patterns and characteristics of the EEG signals. We will explore machine learning approaches, particularly in the realm of convolutional neural networks, CNNs, which are depth of recognizing patterns in visual data. Yes. User defined parameter tuning to accommodate the highly specialized nature of EEG analysis. We will provide interfaces for users to adjust algorithm parameters. This flexibility will enable professionals to calibrate the feature detection to their specific requirements, potentially improving the accuracy of seizure spike detection. Collaboration with medical professionals in developing these features, we will seek partnerships with medical practitioners and researchers. Their expertise is invaluable in fine tuning the application to meet clinical standards and ensure that the tool provides practical benefits in a medical setting. Educational and research use, beyond clinical applications, this feature could serve as an educational tool for students and researchers interested in biomedical signal processing. It would provide an accessible way to visualize and understand the complexities of EEG signals and seizure activities. Incorporating such specialized feature detection would not only broaden the application scope, but also deepen its impact, especially in critical fields such as medical imaging and research. The ability to process and analyze EEG signals effectively could turn the application into a powerful tool for both educational and diagnostic purposes. It looks like, yes, it's just the paragraph there. There's nothing wrong about it, just don't like the language it uses. It's a bit too corporate. We can't leave with that. It's looking pretty good. I would like to publish. We have this file here. It would be a web application. I would like to publish this flask application. We kind of know what to do. This feature extraction, let's check if the folder is correct. H extraction folder looks legit. Now we need to configure our Apache server file. We already have a few web applications. Yeah, we have SSL. That was actually how I commented out. Uncomment the security related lines in your MyApp. WSGI file to enforce HTTPS and secure cookie handling. That should be okay. Funnel. More notes. Always keep your server and application updated with the latest security patches. Regularly backup your application and database. Monitor your application's performance and logs for any unusual activity. This is a high level overview. Each step may involve additional sub steps or considerations depending on your specific server setup, application requirements and security needs. Yeah, right. That's all. Well, not all, but some of the main problems. Right. Now we need to edit our Apache configuration file. Get control shift v instead of right. So we already have a bunch of flask applications. That was the last one we deployed was the EG noise removal duration. Right, so we have nine flask applications deployed this change. So we'll have 10 flask applications to change the name to feature extraction. Yeah, I'll do it manually. Just a visual check. And we can save and check the config test. That syntax is okay. Now there's one bit that we always struggle with. The fact that I was at the IEG noise. I think it's in tournament machine B in the Python code. We check if the call is in deployment or local server. Now that's probably in the JavaScript. Right, this one. Check if we are on production. Yeah, this bit goes somewhere at the beginning because the URL is different. And by the way, nothing we could do quickly. See if Dali 3 can produce generating it. If in this page, you need to have Dali in a separate tab or something. Yeah. I'm sorry for any confusion, but currently I'm unable to directly generate images or interact with Dali. If you're looking to create an image using Dali, you'll typically need to go to the OpenAI Dali website or use the Dali API. Providing a prompt that closely describes the image you want to generate. Yeah, it's all one big beta, isn't it? For instance, if you want to create an image that represents the front end of your application, as described earlier, you might use a prompt like. Yeah, let's try this prompt. See what it gives. A screenshot of a web application for image feature detection, showing side-by-side comparison of an original MRI brain scan and the same scan with feature points highlighted with a modern, clean user interface. Okay. This descriptive prompt could help Dali generate an image that closely matches your application's interface. If you want to proceed with Dali image generation, you will need to visit OpenAI's platform or use their API with the proper access and permissions. Right, actually pretty decent. It's stuffing up the text, you know, to change my front end to look more similar to this. Yeah, probably need something more generic, because this suggests that that's how the application actually looks like, and it does not. I mean, it can just take that bit, it gets from the real thing. If I hadn't, yeah, those images are not great. Yeah, this one could really use an ROI. Yeah, but the main thing closer to home, to implement the application, to do that, to check if in-production. Yeah, because on local server, we just call data and in-production, we need the folder as well. The JavaScript snippet you've provided is checking the window. Location, hostname property to determine if the application is running in a production environment or a local development environment. Depending on the environment, it sets endpoint base to the appropriate URL endpoint for AJAX calls or other HTTP requests. Question is, this J is to include the determinant of the application is running in production environment or local development environment, change all the names accordingly. Yes, less than. Dot type html greater than less than html lang equals n greater than. Give me two responses in a while to determine if your application is running in a production or development environment and change the folder names accordingly. You can use the window location as you've shown in your earlier snippet. You would then use this condition to set the base URL for your fetch calls accordingly. Once is done, twice. Right, I need to do it both for fetch images and fetch detectives. How about process image and then we can point this to the environment. Same for fetch detected type. In this code, images endpoint and detectors endpoint are set based on whether the application is detected as running in production or development. This is determined by checking the hostname against local host and 127.0.0.1. Right, so in local environment it's still working okay. You would replace production underscore images underscore folder and production underscore detectors underscore folder with the actual parts you use in production. The fetch calls within the fetch images and fetch detector types functions then use these dynamic endpoints. Okay, this would work with it. The folder should be fetch images actual image folder. Remember to configure the corresponding routes in your Flask application to serve the correct endpoints based on the environment. The Flask app will need to handle these URLs to route the requests the right resource locations. Okay, can you help me with the previous bit? That doesn't look correct. Yes, that's correct. The idea is to define the paths dynamically based on the environment where the application is running. If the application is running in a production environment you would use the production paths. If it's running in a development environment you would use the development paths. Here's the modified code with placeholders for the production paths. So what do I replace those placeholders? Make sure you replace production underscore images underscore path and production underscore detectors underscore path with the actual directory paths where your production images and detector scripts are located. This allows you to maintain a single code base without having to manually change your URLs when you move between development and production environments. When you deploy to production make sure that the paths you set up in images endpoint and detectors endpoint correspond to real routes on your server that are configured to serve the requests. For instance, if you have an Nginx or Apache server in front of your Flask application you need to ensure that these paths are correctly routed to your Flask app. Right, so this will be my Flask app. Yeah, because if on local server fetch images work in tiering production it should be this folder, this game might as well just try to run it. Make sure on local server the app still works. Okay, we'll take this site down for a few seconds. It's well mean that it will be the improved version of it. Right, the image is flashing there but then disappearing. It must be a problem. The image is flashing once. It's very interesting. Still works on the local server. Okay, might have to continue this later. I'm not too published yet but we'll see in a bit.